Báo cáo khoa học: "Combined One Sense Disambiguation of Abbreviations" potx

Proceedings of ACL-08: HLT, Short Papers (Companion Volume), pages 61–64,
Columbus, Ohio, USA, June 2008.
c
2008 Association for Computational Linguistics
Combined One Sense Disambiguation of Abbreviations


Yaakov HaCohen-Kerner
Department of Computer
Science, Jerusalem College of
Technology (Machon Lev)
21 Havaad Haleumi St., P.O.B.
16031, 91160 Jerusalem, Israel

Ariel Kass
Department of Computer
Science, Jerusalem College of
Technology (Machon Lev)
21 Havaad Haleumi St., P.O.B.
16031, 91160 Jerusalem, Israel

Ariel Peretz
Department of Computer
Science, Jerusalem College of
Technology (Machon Lev)
21 Havaad Haleumi St., P.O.B.
16031, 91160 Jerusalem, Israel



Abstract

A process that attempts to solve abbreviation
ambiguity is presented. Various context-
related features and statistical features have
been explored. Almost all features are domain
independent and language independent. The
application domain is Jewish Law documents
written in Hebrew. Such documents are
known to be rich in ambiguous abbreviations.
Various implementations of the one sense per
discourse hypothesis are used, improving the
features with new variants. An accuracy of
96.09% has been achieved by SVM.
1 Introduction
An abbreviation is a letter or sequence of letters,
which is a shortened form of a word or a sequence
of words, which is called the sense of the
abbreviation. Abbreviation disambiguation means
to choose the correct sense for a specific context.
Jewish Law documents written in Hebrew are
known to be rich in ambiguous abbreviations
(HaCohen-Kerner et al., 2004). They can,
therefore, serve as an excellent test-bed for the
development of models for abbreviation
disambiguation.
As opposed to the documents investigated in
previous systems, Jewish Law documents usually
do not contain the sense of the abbreviations in the
same discourse. Therefore, the abbreviations are
regarded as more difficult to disambiguate.
This research defines features, as well as

experiments with various variants of the one sense
per discourse hypothesis. The developed process
considers other languages and does not define pre-
execution assumptions. The only limitation to this
process is the input itself: the languages of the
different text documents and the man-made
solution database inputted during the learning
process limit the datasets of documents that may be
solved by the resulting disambiguation system.
The proposed system, preserves its portability
between languages and domains because it does
not use any natural language processing (NLP)
sub-system (e.g.: tokenizer and tagger). In this
matter, the system is not limited to any specific
language or dataset. The system is only limited by
the different inputs used during the system’s
learning stage and the set of abbreviations defined.
This paper is organized as follows: Section 2
presents previous systems dealing with
disambiguation of abbreviations. Section 3
describes the features for disambiguation of
Hebrew abbreviations. Section 4 presents the
implementation of the one sense per discourse
hypothesis. Section 5 describes the experiments
that have been carried out. Section 6 concludes and
proposes future directions for research.
2 Abbreviation Disambiguation
The one sense per collocation hypothesis was
introduced by Yarowsky (1993). This hypothesis
states that natural languages tend to use consistent

spoken and written styles. Based on this
hypothesis, many terms repeat themselves with the
same meaning in all their occurrences. Within the
context of determining the sense of an
abbreviation, it may be assumed that authors tend
to use the same words in the vicinity of a specific
long form of an abbreviation. The words may be
reused as indicators of the proper solution of an
additional unknown abbreviation with the same
words in its vicinity. This is the basis for all
contextual features defined in this research.
The one sense per discourse hypothesis (OS)
was introduced by Gale et al. (1992). This
61
hypothesis assumes that in natural languages, there
is a tendency for an author to be consistent in the
same discourse or article. That is, if in a specific
discourse, an ambiguous phrase or term has a
specific meaning, any other subsequent instance of
this phrase or term will have the same specific
meaning. Within the context of determining the
sense of an abbreviation, it may be assumed that
authors tend to use a specific abbreviation in a
specific sense throughout the discourse or article.
Research has been done within this domain,
mainly for English medical documents. Systems
developed by Pakhomov (2002; 2005), Yu et al.
(2003) and Gaudan et al. (2005) achieved 84% to
98% accuracy. These systems used various
machine learning (ML) methods, e.g.: Maximum

Entropy, SVM and C5.0.
In our previous research (HaCohen-Kerner et
al., 2004), we developed a prototype abbreviation
disambiguation system for Jewish Law documents
written in Hebrew, without using any ML method.
The system integrated six basic features: common
words, prefixes, suffixes, two statistical features
and a Hebrew specific feature. It achieved about
60% accuracy while solving 50 abbreviations with
an average of 2.3 different senses in the dataset.
3 Abbreviation Disambiguation Features
Eighteen different features of any abbreviation
instance were defined. They are divided into three
distinct groups, as follows:
Statistical attributes: Writer/Dataset
Common Rule (WC/DS). The most common
solution used for the specific abbreviation by the
discussed writer/ in the entire dataset.
Hebrew specific attribute: Gimatria Rule
(GM). The numerical sum of the numerical values
attributed to the Hebrew letters forming the
abbreviation (HaCohen-Kerner et al., 2004).
Contextual relationship attributes:
1. Prefix Counted Rule (PRC): The selected
sense is the most commonly appended sense by the
specific prefix.
2. Before/After K (1,2,3,4) Words Counted
Rule (BKWC/AKWC): The selected sense is the
most commonly preceded/succeeded sense by the
K specific words in the sentence of the specific

abbreviation instance.
3. Before/After Sentence Counted Rule
(BSC/ASC): The selected sense is the most
commonly preceded/succeeded sense by all the
specific words in the sentence of the specific
abbreviation instance.
4. All Sentence/Article Counted Rule
(AllSC/AllAC): The selected sense is the most
commonly surrounded sense by all the specific
words in the sentence/article of the specific
abbreviation instance.
5. Before/After Article Counted Rule
(BAC/AAC): The selected sense is the most
commonly preceded/succeeded sense by all the
specific words in the article of the specific
abbreviation instance.
4 Implementing the OS Hypothesis
As mentioned above, the basic assumption of the
OS hypothesis is that there exists at least one
solvable abbreviation in the discourse and that the
sense of that abbreviation is the same for all the
instances of this abbreviation in the discourse. The
correctness of all the features was investigated
based on this hypothesis for several variants of
"one sense" based on the discussed discourse: none
(No OS), a sentence (osS), an article (osA) or all
the articles of the writer (osW).
The OS hypothesis was implemented in two
forms. The “pure” form (with the suffix S/A/W
without C) uses the sense found by the majority

voting method for an abbreviation in the discourse
and applies it “blindly” to all other instances.
The “combined” form (with the suffix C) tries to
find the sense of the abbreviation using the
discussed feature only. If the feature is
unsuccessful, then we use the relevant one sense
variant using the majority voting method. This
form is derived from the possibility that more than
one sense may be used within a single discourse
and only instances with an unknown sense
conform to the hypothesis.
The use of the OS hypothesis, in both forms, is
only relevant for context based features, since the
solutions by other features are static and identical
from one instance to another.
Therefore, for each of the 15 context based
features, 6 variants of the hypothesis were
implemented. This produces 90 variants, which
together with the 18 features in their normal form,
results in a total of 108 variants. In addition, the
ML methods were experimented together with the
OS hypothesis. Of the 108 possible variants, for
the 18 features, the best variant for each feature
62
was chosen. In each step, the next best variant is
added, starting from the 2 best variants.
5 Experiments
The examined dataset includes Jewish Law
Documents written by two Jewish scholars: Rabbi
Y. M. HaCohen (1995) and Rabbi O. Yosef (1977;

1986). This dataset includes 564,554 words where
114,814 of them are abbreviations instances, and
42,687 of them are ambiguous. That is, about 7.5%
of the words are ambiguous abbreviations. These
ambiguous abbreviations are instances of a set of
135 different abbreviations. Each one of the
abbreviations has between 2 to 8 relevant possible
senses. The average number of senses for an
abbreviation in the dataset is 3.27.
To determine the accuracy of the system, all the
instances of the ambiguous abbreviations were
solved beforehand. Some of them were based on
published solutions (HaCohen, 1995) and some of
them were solved by experienced readers.
5.1 Results of the variants of OS Hypothesis
The results of the OS hypothesis variants, for all
the features, are presented in Table 1. These results
are obtained without using any ML methods.

Accuracy Percentage %
Use of OS /

Feature
No OS

osS

osSC

osA

osAC

osW

osWC

PRC
33.67

34.41

34.52

52.77

54.54

66.66

71.04

B1WC
56.05

56.41

56.61

67.74

71.84

72.93

82.51

B2WC
55.72

56.23

56.35

69 72.34

74.85

82.84

B3WC
60.54

60.89

61.01

72.67

75.48

75.44

82.86

B4WC
64.49

64.72

64.85

74.29

76.5

75.52

82.2

BSC
75.21

75.18

75.24

76.85

78.15

74.92

78.52

BAC
76 76 76 76.01

76 75.39

76
A1WC 78.79

79.01

79.21

78.72

83.81

76.32

87.75

A2WC
77.57

78.07

78.26

79.15

83.43

78.54

87.62

A3WC
78.64

79.11

79.28

79.61

83 78.19

85.8

A4WC
75.44

79.28

79.5

79.41

82.42

78.01

84.99

ASC
78.59

78.61

78.62

78.25

78.94

77.37

79.04

AAC
75.44

75.44

75.44

75.34

75.44

77.28

75.44

AllSC
77.97

77.97

77.97

77.9

78.02

77.22

78.04

AllAC
74.12

74.12

74.12

74.12

74.12

76.93

74.12

GM 46.82

46.82

46.82

46.82

46.82

46.82

46.82

WC 82.84

82.84

82.84

82.84

82.84

82.84

82.84

DC 78.34

78.34

78.34

78.34

78.34

78.34

78.34

Table 1. Results of the OS Variants for all the Features.

The two best pure features were WC and A1WC
with 82.84% and 78.79% of accuracy, respectively.

The first finding shows that about 83% of the
abbreviations have the same sense in the whole
dataset. The second finding shows that about 79%
of the abbreviations can be solved by the first word
that comes after the abbreviation.

Generally, contextual features based on the
context that comes after the abbreviation, achieve
considerably better results than all other contextual
features. Specifically, the A1WC_osWC feature
variant achieves the best result with 87.75%
accuracy. These results suggest that each
individual abbreviation has stronger relationship to
the words after a specific instance, especially to the
first word.
Almost every feature has at least one variant that
achieves a substantial improvement in results
compared the results achieved by the feature in its
normal form. The average relative improvement is
about 18%.
For all features, except BAC, the best variant
uses the OS implementation with the discourse
defined as the entire dataset. This may be
attributed to the similarity of the different articles
in the dataset. This is supported by the fact that the
best feature, in its normal form, is the WC feature.
In addition, for all but three features (BAC,
AAC, AllAC), the best variant used the combined
form of the OS implementation. This is intuitively
understandable, since “blindly” overwriting
probably erases many successes of the feature in its
normal form.
5.2 The Results of the Supervised ML Methods
Several well-known supervised ML methods have
been selected: artificial neural networks (ANN),
Naïve Bayes (NB), Support Vector Machines

(SVM) and J48 (Witten and Frank, 1999) an
improved variant of the C4.5 decision tree
induction. These methods have been applied with
default values and no feature normalization using
Weka (Witten and Frank, 1999). Tuning is left for
future research. To test the accuracy of the models,
10-fold cross-validation was used.
Table 2 presents the results of these supervised
ML methods, by incrementally combining the best
variant for each feature (according to Table 1).
Table 2 shows that SVM achieved the best result
with 96.09% accuracy. The best improvement is
63
about 13%, from 82.84% accuracy for the best
variant of any feature to 96.02% accuracy. This
table also reveals that incremental combining of
most of the variants leads to better results for most
of the ML methods.

# of
Vari-
ants

Variants /
ML Method
ANN

NB

SVM

J48

2
A1WC_osWC
+A2WC_osWC

91.56 91.40

94.29

91.94

3
+ A3WC_osWC

91.72 91.42

94.43

92.20

4
+ A4WC_osWC

91.75 91.51

94.43

92.34

5
+ B3WC_osWC

92.68 92.11

95.33

93.33

6
+ WC
92.95 92.16

95.71

93.54

7
+ B2WC_osWC

92.81 91.79

95.67

93.59

8
+ B1WC_osWC

92.91 91.06

95.68

93.56

9
+ B4WC_osWC

92.83 91.15

95.62

93.55

10
+ ASC_osWC
92.83 91.10

95.60

93.52

11
+ BSC_osWC
92.95 91.17

95.65

93.58

12
+ DC
92.98 91.17

95.63

93.58

13
+ AllSC_osWC

92.82 91.50

95.63

93.58

14
+ AAC_osW
92.84 91.42

95.59

93.58

15
+ AllAC_osW
93.10 91.43

95.77

93.58

16
+ BAC_osA
93.09 91.28

95.79

93.70

17
+ PRC_osWC
93.25 91.50

96.09

93.71

18
+ GM
93.28 91.52

96.02

93.93

Table 2. The Results of the ML Methods.

The comparison of the SVM results to the
results of previous (Section 2) shows that our
system achieves relatively high accuracy.
However, most previous systems researched
ambiguous abbreviations in the English language,
as well as different abbreviations and texts.
6 Conclusions, Summary and Future Work
This is the first ML system for disambiguation of
abbreviations in Hebrew. High accuracy
percentages were achieved, with improvement
ascribed to the use of OS hypothesis combined
with ML methods. These results were achieved
without the use of any NLP features. Therefore, the
developed system is adjustable to any specific type
of texts, simply by changing the database of texts
and abbreviations.
This system is the first that applies many
versions of the one sense per discourse hypothesis.

In addition, we performed a comparison between
the achievements of four different standard ML
methods, to the goal of achieving the best results,
as opposed to the other systems that mainly
focused on one ML method, each.
Future research directions are: comparison to
abbreviation disambiguation using the standard
bag-of-words or collocation feature
representations, definition and implementation of
other NLP-based features and use of these features
interlaced with the already defined features,

applying additional ML methods, and augmenting
the databases with articles from additional datasets
in the Hebrew language and other languages.
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